ArticleDiagnostics (Basel, Switzerland)2022
Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Diffusion MRI Radiomics Predict Renal Function Decline in ADPKD.Kidney international reports · 2026Article
- Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026Article
- DiagNeXt: A Two-Stage Attention-Guided ConvNeXt Framework for Kidney Pathology Segmentation and Classification.Journal of imaging · 2025Article
- Are Image-Based Deep Learning Algorithms of Kidney Volume in Polycystic Kidney Disease Ready for Clinical Deployment? A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- In-depth 3D exploration of autosomal dominant polycystic kidney disease through light sheet fluorescence microscopy.Scientific reports · 2025Article
- Reporting ADPK Disease Phenotypes on Abdominal Scans.Kidney international reports · 2025Article
- The Role of Baseline Total Kidney Volume Growth Rate in Predicting Tolvaptan Efficacy for ADPKD Patients: A Feasibility Study.Journal of clinical medicine · 2025Article
- Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease.European radiology experimental · 2024Article
- Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease.Bioengineering (Basel, Switzerland) · 2024Article
- Improved predictions of total kidney volume growth rate in ADPKD using two-parameter least squares fitting.Scientific reports · 2024Article
- Automated prognosis of renal function decline in ADPKD patients using deep learning.Zeitschrift fur medizinische Physik · 2024Article
- Test Retest Reproducibility of Organ Volume Measurements in ADPKD Using 3D Multimodality Deep Learning.Academic radiology · 2024Article
- Inspiring Tactics with the Improvement of Mitophagy and Redox Balance for the Development of Innovative Treatment against Polycystic Kidney Disease.Biomolecules · 2024Review
- AI models for automated segmentation of engineered polycystic kidney tubules.Scientific reports · 2024Article
- A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis.Frontiers in neuroscience · 2024Article
- Renal MRI: From Nephron to NMR Signal.Journal of magnetic resonance imaging : JMRI · 2023Review
- Effect of Averaging Measurements From Multiple MRI Pulse Sequences on Kidney Volume Reproducibility in Autosomal Dominant Polycystic Kidney Disease.Journal of magnetic resonance imaging : JMRI · 2023Article
- Abdominal Imaging in ADPKD: Beyond Total Kidney Volume.Journal of clinical medicine · 2023Review
- Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.Scientific reports · 2023Article
- Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease.Investigative and clinical urology · 2023Article
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7 authors.
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Abstract
Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.
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